import math

import torch
import torch.optim as optim

# Codes taken from https://github.com/ikostrikov/pytorch-a3c/
# This enables different processes to share optimizer statistics
# which improves overall agent performance.
# Similar codes can be used for other optimizers too.

class SharedAdam(optim.Adam):
        """Implements Adam algorithm with shared states.
        """

        def __init__(self,
                                 params,
                                 lr=1e-3,
                                 betas=(0.9, 0.999),
                                 eps=1e-8,
                                 weight_decay=0):
                super(SharedAdam, self).__init__(params, lr, betas, eps, weight_decay)

                for group in self.param_groups:
                        for p in group['params']:
                                state = self.state[p]
                                state['step'] = torch.zeros(1)
                                state['exp_avg'] = p.data.new().resize_as_(p.data).zero_()
                                state['exp_avg_sq'] = p.data.new().resize_as_(p.data).zero_()

        def share_memory(self):
                for group in self.param_groups:
                        for p in group['params']:
                                state = self.state[p]
                                state['step'].share_memory_()
                                state['exp_avg'].share_memory_()
                                state['exp_avg_sq'].share_memory_()

        def step(self, closure=None,lr=None):
                """Performs a single optimization step.
                Arguments:
                        closure (callable, optional): A closure that reevaluates the model
                                and returns the loss.
                """
                loss = None
                if closure is not None:
                        loss = closure()

                for group in self.param_groups:
                        for p in group['params']:
                                if p.grad is None:
                                        continue

                                if lr is not None:
                                        pass
                                else:
                                        lr = group['lr']

                                grad = p.grad.data
                                state = self.state[p]

                                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                                beta1, beta2 = group['betas']

                                state['step'] += 1

                                if group['weight_decay'] != 0:
                                        grad = grad.add(group['weight_decay'], p.data)

                                # Decay the first and second moment running average coefficient
                                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)

                                denom = exp_avg_sq.sqrt().add_(group['eps'])

                                bias_correction1 = 1 - beta1 ** state['step'].item()
                                bias_correction2 = 1 - beta2 ** state['step'].item()
                                step_size = lr * math.sqrt(
                                        bias_correction2) / bias_correction1

                                p.data.addcdiv_(-step_size, exp_avg, denom)

                return loss
